Plot time series for NDVI on Landsat8

Question

I want to select a region on a map and get the time series for NDVI based on Landsat 8 imagery. If possible, it would be better if I can get the image row and path through matching the Landsat tiles by my area of interest.

However, I need to select Landsat 8 C2L2 image, filter that for specific time and region with least cloud coverage then get image acquistion time to plot NDVI as a time series.

l8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2");

var L8=l8.filterDate('2021-01-01', '2021-11-11')
         .select(['SR_B[1-7]','ST_B10'])
         .filter(ee.Filter.eq('WRS_PATH', 178))
         .filter(ee.Filter.eq('WRS_ROW', 34))
//         .copyProperties(l8, ['system:time_start']);

When the copyProperties line is uncommented, most of the sequentional commands fail to run

print('L8 Mosaic',L8.mosaic())

I can sort images by time here:

var L8Sorted=L8.sort('system:time_start', false)//.limit(10)
print('L8Sorted Time',L8Sorted)


function applyScaleFactors(image) {
  var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
  var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
  return image.addBands(opticalBands, null, true)
              .addBands(thermalBands, null, true);
}

L8 = L8.map(applyScaleFactors);

I want to apply NDVI computation on each image on different dates for my area of interest to produce a time series. I am unsure if mosaic is right for this purpose.

var NDVI=L8.mosaic().normalizedDifference(['SR_B5','SR_B4'])

Above mentioned NDVI returns just one image.

And I can't get their time to use in ui.chart function.

How can I achieve my aim?

Answer 1

here is the last complete code after a while

// first select parsels from TKGM
var P1 = ee.FeatureCollection('users/Solmaz/TKGM/Agriculture1');
var P2 = ee.FeatureCollection('users/Solmaz/TKGM/Agriculture2');
var P3 = ee.FeatureCollection('users/Solmaz/TKGM/Agriculture3');
var P4 = ee.FeatureCollection('users/Solmaz/TKGM/Agriculture4');
var P5 = ee.FeatureCollection('users/Solmaz/TKGM/Agriculture5');
var P6 = ee.FeatureCollection('users/Solmaz/TKGM/Forest1');
var P7 = ee.FeatureCollection('users/Solmaz/TKGM/Forest2');
var P8 = ee.FeatureCollection('users/Solmaz/TKGM/Mine_Manual');


var p1 = P1.geometry().coordinates()
var p2 = P2.geometry().coordinates()
var p3 = P3.geometry().coordinates()
var p4 = P4.geometry().coordinates()
var p5 = P5.geometry().coordinates()
var p6 = P6.geometry().coordinates()
var p7 = P7.geometry().coordinates()
var p8 = P8.geometry().coordinates()

var PP1 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p1))
    .set('label', 'Agriculture1')
    .set('value', '0')
]);
var PP2 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p2))
    .set('label', 'Agriculture2')
    .set('value', '1')
]);
var PP3 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p3))
    .set('label', 'Agriculture3')
    .set('value', '2')
]);
var PP4 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p4))
    .set('label', 'Agriculture4')
    .set('value', '3')
]);
var PP5 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p5))
    .set('label', 'Agriculture5')
    .set('value', '4')
]);
var PP6 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p6))
    .set('label', 'Forest1')
    .set('value', '5')
]);
var PP7 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p7))
    .set('label', 'Forest2')
    .set('value', '6')
]);
var PP8 = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(p8))
    .set('label', 'Mine')
    .set('value', '7')
]);


var Polygons = PP1.merge(PP2).merge(PP3).merge(PP4).merge(PP5).merge(PP6).merge(PP7).merge(PP8);
print('Polygons',Polygons)



// Load MODIS vegetation indices data and subset a decade of images.
var vegIndices = ee.ImageCollection('MODIS/006/MOD13Q1')
                     .filter(ee.Filter.date('2020-01-01', '2021-11-18'))
                     .select(['NDVI', 'EVI']);

// Define the chart and print it to the console.
var chart =
    ui.Chart.image
        .seriesByRegion({
          imageCollection: vegIndices,
          band: 'NDVI',
          regions: Polygons,
          reducer: ee.Reducer.mean(),
          scale: 250,
          seriesProperty: 'label',
          xProperty: 'system:time_start'
        })
        .setOptions({
          title: 'Average NDVI Value by Date',
          hAxis: {title: 'Date', titleTextStyle: {italic: false, bold: true}},
          vAxis: {
            title: 'NDVI (x1e4)',
            titleTextStyle: {italic: false, bold: true}
          },
          lineWidth: 4,
          colors: ['ffffe5', 'f7fcb9','d9f0a3','addd8e','a1d99b','006837','00441b','980043'],
        });
print(chart);

Map.addLayer(Polygons,{},'Sample Regions')


Map.addLayer(ee.Image().paint(Polygons, 0, 4), {palette: ['#dd1c77','#dd1c77']}, 'TKGM Parsels'); 
//Map.addLayer(ee.Image().paint(P2, 0, 4), {palette: ['#dd1c77','#dd1c77']}, 'Forest'); 
Map.centerObject(Polygons,10)